<div id="principal">
<p><strong>Dipankar Bandyopadhyay</strong> -  <a href="http://www.biostat.umn.edu/~dipankar">http://www.biostat.umn.edu/~dipankar</a> </p>
<p>University of Minnesota, Minneapolis, USA</p>
</font>




<ul style="text-align: justify;">

<li><font face="Arial" size="2" color="#000000">
<strong>T&iacute;tulo:</strong>
</font>
</li>
<p>Exploring periodontal disease progression as a spatially-referenced phenomemon</p>

<li><font face="Arial" size="2" color="#000000">
<strong>Resumo:</strong>
</font>
</li>
<p id="resumo">Periodontal disease is the primary cause of adult tooth loss. One of the prime objectives of the National Institute of Dental and Craniofacial Research (NIDCR).s 2009-2013 strategic plan is &lsquo;To bring the best science to bear on problems in oral, dental and craniofacial health&rsquo;. However, the analysis of periodontal disease data comes with several interesting challenges. These clustered data are a mix of binary and continuous responses, warranting joint models to assess the true disease status. Moreover, perio-progression can be conjectured to be *spatially-referenced*, i.e. a diseased site might influence the status of a set of neighboring sites within a tooth, or a proximal tooth. Also, it is likely that the number and location of absent teeth can be informative about the subject's oral health in that region. In this talk, we develop a multivariate spatial framework for these data using a Bayesian paradigm that jointly models the  inary and continuous responses as a function of a single latent spatial process representing general periodontal health. Under a shared random effects framework, this spatial process also models the location of an absent tooth.  Influence diagnostics are carried out to identify the sources of data which are mostly informative of the covariate effects. Using both simulated and real data, we demonstrate that exploiting spatial associations and jointly modeling the  response and location of absent tooth mitigates the problems posed by these informatively present data.</p>
<p>This is joint work with Brian Reich at North Carolina State University.</p>

</div>
